Prostate cancer: Improved tissue characterization by temporal modeling of radio-frequency ultrasound echo data

Abstract

Despite recent advances in clinical oncology,prostate cancer remains a major health concern in men,where current detection techniques still lead to both over- and under-diagnosis. More accurate prediction and detection of prostate cancer can improve disease management and treatment outcome. Temporal ultrasound is a promising imaging approach that can help identify tissue-specific patterns in time-series of ultrasound data and,in turn,differentiate between benign and malignant tissues. We propose a probabilistic-temporal framework,based on hidden Markov models,for modeling ultrasound time-series data obtained from prostate cancer patients. Our results show improved prediction of malignancy compared to previously reported results,where we identify cancerous regions with over 88% accuracy. As our models directly represent temporal aspects of the data,we expect our method to be applicable to other types of cancer in which temporal-ultrasound can be captured.